The Evolution Spectrum: Planning Continuous and Drastic Improvements with AI
How emerging technologies are reshaping product evolution strategies
As product managers taking over existing products, we often find ourselves navigating two distinct improvement approaches: the steady path of continuous improvement and the bold leap of drastic change. The art of product management lies in knowing when each approach serves your product best—and increasingly, AI is becoming a powerful ally in both defining and executing these strategies.
🔄 The Continuous Improvement Mindset
Continuous improvement focuses on making small, regular enhancements to an existing product. This approach:
🛡️ Preserves existing user workflows and mental models
📉 Reduces implementation risk through smaller change increments
🧩 Allows for more frequent deployment and feedback cycles
💰 Typically requires less upfront investment per release
Planning Effective Continuous Improvement
📊 Establish Clear Baseline Metrics
Define specific metrics you want to improve
Set realistic improvement targets
🎯 Create Prioritized Opportunity Backlogs
Score opportunities based on effort-to-impact ratios
Consider dependencies and sequencing requirements
🔍 Implement Structured Experimentation
Design experiments with clear success criteria
Use A/B testing for data-driven decisions
🔄 Develop Regular Release Cadences
Establish predictable improvement cycles
Communicate timelines to stakeholders
🚀 The Drastic Improvement Approach
Drastic improvements involve fundamental rethinking of how a product works or the value it delivers.
These become necessary when:
🛑 The current architecture has reached its scaling limits
🏁 Competitors have leap-frogged your offering
↔️ Market conditions have dramatically shifted
💡 New technologies enable fundamentally better approaches
Planning Successful Drastic Improvements
🌉 Create Clear Transition Bridges
Map migration paths for existing users and data
Develop comprehensive change management plans
🛡️ Build Safety Nets and Rollback Plans
Establish specific go/no-go criteria
Implement staged rollouts with circuit breakers
💪 Secure Strong Executive Sponsorship
Align stakeholders on the case for change
Obtain resource commitments that reflect project scale
🔭 Develop Longer Feedback Horizons
Set realistic timelines for seeing impact
Identify leading indicators of success
🤖 How AI Is Transforming Improvement Planning
🔍 AI for Helping PMs Identify Improvement Opportunities
For Continuous Improvement:
AI can analyse customer feedback across multiple channels to identify trending pain points
AI tools can categorize and prioritize feature requests based on customer segment and business impact
AI systems can monitor competitor product updates and identify feature gaps relevant to your market position
AI can analyse support tickets to identify recurring issues that impact customer satisfaction
For Drastic Improvements:
AI can detect emerging user behaviour patterns that suggest fundamental shifts in needs
AI can analyse market trends across industries to identify disruptive business models that could be applied
AI can evaluate your product architecture against technology trends to identify modernization opportunities
AI can synthesize inputs from diverse stakeholders to identify potential strategic pivots
📝 AI for Supporting PM Planning and Decision-Making
For Continuous Improvement:
AI can generate data-backed hypotheses for improvement initiatives, saving PMs research time
AI can draft detailed user stories and acceptance criteria based on high-level improvement goals
AI tools can simulate user journeys to predict the impact of proposed changes on key metrics
AI can perform rapid competitive analysis to prioritize feature enhancements against market offerings
For Drastic Improvements:
AI can develop multiple product strategy scenarios with projected outcomes for executive presentations
AI can create detailed transition plans for moving users from legacy to new systems
AI can generate comprehensive stakeholder impact assessments, identifying potential resistance points
AI can develop detailed roadmaps that balance technical feasibility with business objectives
💼 AI for Product Management Communication and Alignment
For Continuous Improvement:
AI can generate targeted stakeholder updates based on role and interest area
AI can create persuasive business cases for prioritizing specific improvements
AI can prepare tailored presentations that explain technical concepts to non-technical stakeholders
AI can draft implementation briefs that clearly communicate PM intentions to development teams
For Drastic Improvements:
AI can create comprehensive change management communications plans
AI can develop executive-ready presentations that make the case for significant investment
AI can generate training materials for sales and support teams explaining new product approaches
AI can facilitate cross-functional alignment by translating concerns across different team perspectives
📊 AI for Product Success Measurement and Storytelling
For Continuous Improvement:
AI can create custom dashboards that highlight the cumulative impact of incremental changes
AI can perform multivariate analysis to isolate the effects of specific improvements
AI can generate automatic insights from product analytics that might be missed by human analysis
AI can identify leading indicators that predict long-term success of recent changes
For Drastic Improvements:
AI can develop before-and-after customer journey analyses to demonstrate transformation impact
AI can create compelling data visualizations for executive presentations on major initiatives
AI can track sentiment across channels to measure market reception to significant product changes
AI can connect product metrics to business outcomes, showing the ROI of transformative efforts
🧩 How Continuous Improvement Creates Space for Drastic Change
While these approaches might seem opposed, strategic continuous improvement actually creates the necessary foundation for successful drastic transformations.
🏗️ Building the Platform for Bold Changes
Effective continuous improvement:
🛡️ Stabilizes Core Functionality
By systematically addressing technical debt and performance issues, CI creates a stable foundation that can better withstand the stresses of major changes
Products with lower defect rates and greater reliability create customer goodwill that provides breathing room for more disruptive innovations
💰 Generates Resource Capacity
Efficiency gains from ongoing optimization free up engineering resources
Enhanced user experience from incremental improvements often increases revenue, creating financial capacity for larger initiatives
🧠 Builds Institutional Knowledge
Continuous improvement processes generate deep product understanding
Teams develop resilience and change management capabilities that become crucial during drastic transformations
🔬 Creates Low-Risk Testing Grounds
A/B testing infrastructure built for small improvements provides mechanisms for validating drastic change concepts
Feature flag systems enable controlled exposure of transformational capabilities
⚖️ The Portfolio Balance Approach
Consider allocating your improvement efforts according to this ratio:
70% Continuous Core Improvements
Refinements to existing features
Performance optimizations and bug fixes
These efforts maintain product health while creating capacity for bigger changes
20% Substantial Extensions
New features that build on existing foundations
Significant UX overhauls of specific sections
These bridge the gap between maintenance and transformation
10% Explorative Reinvention
Experimentation with radical new approaches
Architectural reimagining
These plant seeds for future drastic improvements
🚦 The Decision Framework: Continuous vs. Drastic
Key questions to guide your approach:
📏 Scale of Gap Analysis
How large is the gap between current capabilities and desired outcomes?
⏱️ Time Horizon Assessment
What is your timeframe for achieving the desired improvement?
🏗️ Architectural Capacity Evaluation
Can your current architecture support the envisioned future state?
👥 User Disruption Tolerance
How sensitive are your users to disruptive changes?
💰 Resource Availability Analysis
Do you have the resources required for a major overhaul?
📈 Getting Started: Your AI-Powered Improvement Plan
Foundation Building
Audit Your Current Improvement Processes
Document how improvements are currently identified and measured
Map current data flows related to product improvements
Establish AI-Ready Data Practices
Standardize improvement documentation formats
Implement consistent metrics tracking for all changes
Pilot Basic AI Analysis Tools
Implement simple NLP for user feedback analysis
Test AI-powered prioritization of small improvements
Capability Expansion
Implement AI-Enhanced Opportunity Identification
Deploy user behaviour analysis
Set up automated competitive intelligence monitoring
Develop AI-Assisted Specification Processes
Train teams to effectively prompt AI for requirements
Create templates for AI-human collaborative specification
Establish Portfolio Optimization Systems
Implement AI models to analyse improvement allocation
Create scenario planning tools for different improvement mixes
🌱 Conclusion: The Symbiotic Relationship Between Improvement Types
The most successful product evolutions come from teams that understand the symbiotic relationship between continuous improvement and drastic change. Rather than viewing these as competing approaches, effective product managers use continuous improvement to build the foundation, resources, and capabilities that make transformative leaps possible.
AI enhances this relationship by providing the insights needed to balance your improvement portfolio effectively. Well-implemented AI systems can simultaneously optimize your current product while identifying opportunities for revolutionary change—helping you leverage today's incremental gains to fuel tomorrow's bold innovations.
This article is part of our ongoing series at Product Forward, focused on helping product managers successfully evolve existing products. What's your experience with balancing continuous and drastic improvements? How are you incorporating AI into your product evolution strategy? Share your thoughts in the comments below.